说明：测试hadoop自带的实例 wordcount程序（此程序统计每个单词在文件中出现的次数）
2.6.0版本jar程序的路径是
/usr/local/hadoop-2.6.0/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.6.0.jar
一、在本地创建目录和文件
创建目录：
mkdir /home/hadoop/input
cd /home/hadoop/input
创建文件：
touch wordcount1.txt
touch wordcount2.txt
二、添加内容
echo "Hello World" > wordcount1.txt
echo "Hello Hadoop" > wordcount2.txt
三、在hdfs上创建input目录
hadoop fs -mkdir /input
四、拷贝文件到/input目录
hadoop fs -put /home/hadoop/input/* /input
五、执行程序
hadoop jar /usr/local/hadoop-2.6.0/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.6.0.jar wordcount /input /output
说明：wordcount为程序的主类名， /input 输入目录 /output 输出目录（输出目录不能存在）
六、执行过程信息
15/04/14 15:55:03 INFO client.RMProxy: Connecting to ResourceManager at hdnn140/192.168.152.140:8032
15/04/14 15:55:04 INFO input.FileInputFormat: Total input paths to process : 2
15/04/14 15:55:04 INFO mapreduce.JobSubmitter: number of splits:2
15/04/14 15:55:05 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1428996061278_0002
15/04/14 15:55:05 INFO impl.YarnClientImpl: Submitted application application_1428996061278_0002
15/04/14 15:55:05 INFO mapreduce.Job: The url to track the job: http://hdnn140:8088/proxy/application_1428996061278_0002/
15/04/14 15:55:05 INFO mapreduce.Job: Running job: job_1428996061278_0002
15/04/14 15:55:17 INFO mapreduce.Job: Job job_1428996061278_0002 running in uber mode : false
15/04/14 15:55:17 INFO mapreduce.Job: map 0% reduce 0%
15/04/14 15:56:00 INFO mapreduce.Job: map 100% reduce 0%
15/04/14 15:56:10 INFO mapreduce.Job: map 100% reduce 100%
15/04/14 15:56:11 INFO mapreduce.Job: Job job_1428996061278_0002 completed successfully
15/04/14 15:56:11 INFO mapreduce.Job: Counters: 49
File System Counters
FILE: Number of bytes read=55
FILE: Number of bytes written=316738
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=235
HDFS: Number of bytes written=25
HDFS: Number of read operations=9
HDFS: Number of large read operations=0
HDFS: Number of write operations=2
Job Counters
Launched map tasks=2
Launched reduce tasks=1
Data-local map tasks=2
Total time spent by all maps in occupied slots (ms)=83088
Total time spent by all reduces in occupied slots (ms)=7098
Total time spent by all map tasks (ms)=83088
Total time spent by all reduce tasks (ms)=7098
Total vcore-seconds taken by all map tasks=83088
Total vcore-seconds taken by all reduce tasks=7098
Total megabyte-seconds taken by all map tasks=85082112
Total megabyte-seconds taken by all reduce tasks=7268352
Map-Reduce Framework
Map input records=2
Map output records=4
Map output bytes=41
Map output materialized bytes=61
Input split bytes=210
Combine input records=4
Combine output records=4
Reduce input groups=3
Reduce shuffle bytes=61
Reduce input records=4
Reduce output records=3
Spilled Records=8
Shuffled Maps =2
Failed Shuffles=0
Merged Map outputs=2
GC time elapsed (ms)=1649
CPU time spent (ms)=4260
Physical memory (bytes) snapshot=280866816
Virtual memory (bytes) snapshot=2578739200
Total committed heap usage (bytes)=244625408
Shuffle Errors
BAD_ID=0
CONNECTION=0
IO_ERROR=0
WRONG_LENGTH=0
WRONG_MAP=0
WRONG_REDUCE=0
File Input Format Counters
Bytes Read=25
File Output Format Counters
Bytes Written=25
七、完成后查看输出目录
hadoop fs -ls /output
八、查看输出结果
hadoop fs -cat /output/part-r-00000
九、完成
